Self-Regulated Learning Behaviors in Learning Management Systems and Their Implications for Academic Achievement in Higher Education

• In Event: Advances in Self-Regulated Learning Research Using Learning Technologies: A Synthesis of the Empirical Evidence

Abstract

Purpose. Learning management systems (LMSs) are the most heavily relied upon advanced learning technology in higher education contexts (Green, 2019). Instructors commonly use LMS sites to post syllabi and grades, and some post rich digital resources that can be used for learning. Use of resources correlates with achievement, and when instructional designers partner with seasoned course instructors, learning resources can both deliver course content and enable students to engage in cognitive and metacognitive processes that characterize self-regulated learning (SRL; Authors, Date; Winne & Hadwin, 2008; Zimmerman, 2013). Students’ use of resources provides insight into the type, frequency, and timing of their engagement with content meant to promote SRL processes (Authors, Date). The purpose of this study is to apply a theoretical mapping of SRL processes to digital resources that afford them, and to examine how the SRL events that students conduct in large introductory lecture courses predict academic achievement.
Theoretical framework. SRL frameworks describe a cycle of task definition, goal setting, planning, enactment of cognitive strategies, and evaluation, overseen by metacognitive monitoring processes. Instructors can provide resources such as syllabi, schedules, policies, and learning objectives to afford task definition and planning, and to implicitly frame students’ goals. Digital course content affords opportunities to engage in cognitive strategies while reading text, viewing videos and simulations, and engaging self-quizzing, and self-explanation. Adaptive features of LMSs can provide performance feedback to students, which can inform their metacognitive judgments and guide adaption. By identifying the SRL processes that LMS-hosted resources support and enriching digital trace data to reflect these processes, researchers can observe students’ SRL and their effects (Table 1). Prior research has shown strong relationships between task definition (e.g., inspecting assignment instructions), planning (e.g., using the course schedule), rehearsal and monitoring (e.g., completing practice problems), and help-seeking (e.g., scheduling an office hour visit).
Methods & Data. We enriched the data of 477 introductory biology students (Mage = 18.74, 70.3% female, 18.95% under-represented minority group, 23.62% first-generation college students) to reflect their SRL behaviors during a semester and used path analyses to examine how these behaviors predict academic achievement. We queried timestamped learning events from university server logs, applied the SRL schema to enrich the data, and aligned behaviors to achievement data from instructor gradebooks.
Results. Prior knowledge predicted students’ behaviors that reflect SRL processes, which predicted achievement (Figure 1). Those who acquired information resources (lessons, readings) and engaged in self-assessment on the LMS (and attended review activities on campus) that afforded opportunities to engage in cognitive strategies and metacognitive monitoring performed better in the course.
Significance. Mapping the digital resources that afford SRL enables observation of the ways students undertake and benefit from such behaviors. These behavior-based analyses can refine theoretical assumptions in SRL frameworks, and also provide insight to educators who wish to design more effective courses and scaffold students’ use of resources for learning. When observed early in a semester, these events can also be used to identify and then support students whose behavior mirrors those of past struggling students.

Authors

• Matthew L. Bernacki, University of North Carolina Chapel Hill

• Robert D Plumley, University of North Carolina - Chapel Hill

• Mladen Rakovic, Simon Fraser University

• Kelly Hogan, University of North Carolina - Chapel Hill

• Mara Evans, UNC Department of Biology

• Kathleen M. Gates, University of North Carolina - Chapel Hill

• Abigail Panter, University of North Carolina - Chapel Hill

• Jeff A. Greene, University of North Carolina - Chapel Hill